The Research Of Analog Circuit Fault Diagnosis Approaches Based On Network Decomposition And Neural Networks

For several decades, fault diagnosis of analog circuits, the forefront of modern circuit theory, has a series of diagnosis theory and methods. However, complicated theory and poor practicability of these methods make these methods'application foreground far from expectation of people.Based on the network decomposition approach the fault diagnosis approach at subnetwork level decomposes the large scale circuit into several suhnetworks and then diagnoses the subnetworks respectively. So it can simplify the computation. It is fast and effective. Along with development of electronic technology, especially VLSI and mixed signal circuits,where there are physical or functional blocks and subnetworks which are indiscerptible in the network, it shows its practical value.This paper presents a approach of module level fault diagnosis for large-scale tolerance analog circuit based on the neural network and the crossover tearing technology according to conventional circuit decomposition technology in large-scale circuits. Through the simulation of large-scale circuit simulation proved that use the crossover tearing technology could detailed network structure, simplify the diagnostic process, and the neural network can parallel deal with the diagnosis information, and the logic operation can judge the information of the multi-fault. The illustrative simulation shows that it can increase the diagnosis speed and decrease the workload before test.This paper mainly researches ANN based methods for fault diagnosis of analog circuits with tolerances,Analyse the theory in fault diagnosis of analog circuits with neural network, presents some improved ANN based methods which can decrease diagnosis time and enhance diagnosis efficiency for analog circuits with tolerances. Mainly to the dissemination of error back propogation Neural Network (BP). The paper mainly did the dissemination of error neural network (BP) research and gave recommendations on ways to improve BP algorithm. By using better diagnostic techniques with the neural networks legend, memory and reasoning functions and tolerant nature, robustness and good nonlinear, it can better realized the fault...